7 steps for executing a successful data science strategy

By David Stodder, TDWI

Data science is a hot topic among business and IT leaders. Excitement about the potential benefits is tempered, however, by anxiety over finding, hiring and training data scientists. Not to mention the difficulty of defining the term within the context of an organization’s goals.

The TDWI Checklist Report, Seven Steps for Executing a Successful Data Science Strategy, has plenty of good tips for making your foray into data science a success.

Data science often points to the need for change – and change can be difficult.

David StodderDirector of Research for BI,
TDWI

A few nuggets from the report

Identify key business drivers. This one is easy. Before embarking on a data science project, the first question to ask is a simple one: Do we need data science? There are many areas where data science could contribute to business success. Examine what value data science can bring to your organization.

Create an effective team. Rather than focusing on finding one or a few individuals who seem to be able to do it all, maybe a wiser course is to develop a stable team that brings together the talents of multiple experts. You may be able to look internally and find what you need.

Emphasize communication skills. Organizations that use data science successfully almost universally point to communication as a key ingredient to their success. Insights provided by analytics are of little value unless the findings and their value can be articulated.

Use visualization and data storytelling. Visualization enables data storytelling. This hot trend fuses visualization, data analysis, and usually verbal or written discussion, often in an infographic, to provide interpretation of data science results and why they are significant. Encourage storytelling and provide training.

Give data science teams access to all the data. Data science is often closely associated with the desire to analyze semi- and unstructured data because these sources are growing rapidly and have been analyzed little. Use raw data and include someone on the team who is comfortable working with it.

Prepare data science processes for operationalizing analytics. Data science teams must focus on reducing the time it takes to develop and deploy analytic models. Along with process improvements, organizations can take advantage of new technology practices such as in-database scoring, which can help eliminate time-consuming data movement and make models available for multiple applications. Use your analytical results.